Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy

IntroductionDeep learning (DL) models predicting biomarker expression in images of hematoxylin and eosin (H&E)-stained tissues can improve access to multi-marker immunophenotyping, crucial for therapeutic monitoring, biomarker discovery, and personalized treatment development. Conventionally, th...

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Ngā taipitopito rārangi puna kōrero
Ngā kaituhi matua:	Abu Bakr Azam, Felicia Wee, Juha P. Väyrynen, Willa Wen-You Yim, Yue Zhen Xue, Bok Leong Chua, Jeffrey Chun Tatt Lim, Aditya Chidambaram Somasundaram, Daniel Shao Weng Tan, Angela Takano, Chun Yuen Chow, Li Yan Khor, Tony Kiat Hon Lim, Joe Yeong, Mai Chan Lau, Yiyu Cai
Hōputu:	Artigo
Reo:	Inglês
I whakaputaina:	Frontiers Media S.A. 2024-06-01
Rangatū:	Frontiers in Immunology
Ngā marau:	virtual staining CD3 Pix2Pix generative adversarial network (P2P-GAN) tumor-infiltrating lymphocytes (TILs) hematoxylin and eosin (H&E) ground truth cell label
Urunga tuihono:	https://www.frontiersin.org/articles/10.3389/fimmu.2024.1404640/full
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Training immunophenotyping deep learning models with the same-section ground truth cell label derivation method improves virtual staining accuracy

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